{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pybel pybel-tools networkx matplotlib seaborn pandas -q\n",
        "\n",
        "import pybel\n",
        "import pybel.dsl as dsl\n",
        "from pybel import BELGraph\n",
        "from pybel.io import to_pickle, from_pickle\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "print(\"PyBEL Advanced Tutorial: Biological Expression Language Ecosystem\")\n",
        "print(\"=\" * 65)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7t_SzHZzL5X",
        "outputId": "593fb7b0-c23b-4a86-ef37-6b31defcb93b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyBEL Advanced Tutorial: Biological Expression Language Ecosystem\n",
            "=================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n1. Building a Biological Knowledge Graph\")\n",
        "print(\"-\" * 40)\n",
        "\n",
        "graph = BELGraph(\n",
        "    name=\"Alzheimer's Disease Pathway\",\n",
        "    version=\"1.0.0\",\n",
        "    description=\"Example pathway showing protein interactions in AD\",\n",
        "    authors=\"PyBEL Tutorial\"\n",
        ")\n",
        "\n",
        "app = dsl.Protein(name=\"APP\", namespace=\"HGNC\")\n",
        "abeta = dsl.Protein(name=\"Abeta\", namespace=\"CHEBI\")\n",
        "tau = dsl.Protein(name=\"MAPT\", namespace=\"HGNC\")\n",
        "gsk3b = dsl.Protein(name=\"GSK3B\", namespace=\"HGNC\")\n",
        "inflammation = dsl.BiologicalProcess(name=\"inflammatory response\", namespace=\"GO\")\n",
        "apoptosis = dsl.BiologicalProcess(name=\"apoptotic process\", namespace=\"GO\")\n",
        "\n",
        "\n",
        "graph.add_increases(app, abeta, citation=\"PMID:12345678\", evidence=\"APP cleavage produces Abeta\")\n",
        "graph.add_increases(abeta, inflammation, citation=\"PMID:87654321\", evidence=\"Abeta triggers neuroinflammation\")\n",
        "\n",
        "tau_phosphorylated = dsl.Protein(name=\"MAPT\", namespace=\"HGNC\",\n",
        "                                variants=[dsl.ProteinModification(\"Ph\")])\n",
        "graph.add_increases(gsk3b, tau_phosphorylated, citation=\"PMID:11111111\", evidence=\"GSK3B phosphorylates tau\")\n",
        "graph.add_increases(tau_phosphorylated, apoptosis, citation=\"PMID:22222222\", evidence=\"Hyperphosphorylated tau causes cell death\")\n",
        "graph.add_increases(inflammation, apoptosis, citation=\"PMID:33333333\", evidence=\"Inflammation promotes apoptosis\")\n",
        "\n",
        "graph.add_association(abeta, tau, citation=\"PMID:44444444\", evidence=\"Abeta and tau interact synergistically\")\n",
        "\n",
        "print(f\"Created BEL graph with {graph.number_of_nodes()} nodes and {graph.number_of_edges()} edges\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nu1hIAVVzNee",
        "outputId": "c8cf0797-7404-4801-e29b-30f540b4342c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "1. Building a Biological Knowledge Graph\n",
            "----------------------------------------\n",
            "Created BEL graph with 7 nodes and 8 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n2. Advanced Network Analysis\")\n",
        "print(\"-\" * 30)\n",
        "\n",
        "degree_centrality = nx.degree_centrality(graph)\n",
        "betweenness_centrality = nx.betweenness_centrality(graph)\n",
        "closeness_centrality = nx.closeness_centrality(graph)\n",
        "\n",
        "most_central = max(degree_centrality, key=degree_centrality.get)\n",
        "print(f\"Most connected node: {most_central}\")\n",
        "print(f\"Degree centrality: {degree_centrality[most_central]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeB8SV4izVme",
        "outputId": "0201fc54-8c82-4ae7-e76d-ae2b9a0b28b2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "2. Advanced Network Analysis\n",
            "------------------------------\n",
            "Most connected node: p(CHEBI:Abeta)\n",
            "Degree centrality: 0.667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n3. Biological Entity Classification\")\n",
        "print(\"-\" * 35)\n",
        "\n",
        "node_types = Counter()\n",
        "for node in graph.nodes():\n",
        "    node_types[node.function] += 1\n",
        "\n",
        "print(\"Node distribution:\")\n",
        "for func, count in node_types.items():\n",
        "    print(f\"  {func}: {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K53J4XCYzY5y",
        "outputId": "45213c44-1d3f-4350-fda3-567ca3f14b47"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "3. Biological Entity Classification\n",
            "-----------------------------------\n",
            "Node distribution:\n",
            "  Protein: 5\n",
            "  BiologicalProcess: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n4. Pathway Analysis\")\n",
        "print(\"-\" * 20)\n",
        "\n",
        "proteins = [node for node in graph.nodes() if node.function == 'Protein']\n",
        "processes = [node for node in graph.nodes() if node.function == 'BiologicalProcess']\n",
        "\n",
        "print(f\"Proteins in pathway: {len(proteins)}\")\n",
        "print(f\"Biological processes: {len(processes)}\")\n",
        "\n",
        "edge_types = Counter()\n",
        "for u, v, data in graph.edges(data=True):\n",
        "    edge_types[data.get('relation')] += 1\n",
        "\n",
        "print(\"\\nRelationship types:\")\n",
        "for rel, count in edge_types.items():\n",
        "    print(f\"  {rel}: {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0FD9gwZzeB3",
        "outputId": "3f2f5f21-463c-4d31-9e6c-2da36b95213f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "4. Pathway Analysis\n",
            "--------------------\n",
            "Proteins in pathway: 5\n",
            "Biological processes: 2\n",
            "\n",
            "Relationship types:\n",
            "  increases: 5\n",
            "  association: 2\n",
            "  hasVariant: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n5. Literature Evidence Analysis\")\n",
        "print(\"-\" * 32)\n",
        "\n",
        "citations = []\n",
        "evidences = []\n",
        "for _, _, data in graph.edges(data=True):\n",
        "    if 'citation' in data:\n",
        "        citations.append(data['citation'])\n",
        "    if 'evidence' in data:\n",
        "        evidences.append(data['evidence'])\n",
        "\n",
        "print(f\"Total citations: {len(citations)}\")\n",
        "print(f\"Unique citations: {len(set(citations))}\")\n",
        "print(f\"Evidence statements: {len(evidences)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbYu8U4Lzjwv",
        "outputId": "26d48511-b19e-4110-8685-705a89e266ab"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "5. Literature Evidence Analysis\n",
            "--------------------------------\n",
            "Total citations: 7\n",
            "Unique citations: 6\n",
            "Evidence statements: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n6. Subgraph Analysis\")\n",
        "print(\"-\" * 22)\n",
        "\n",
        "inflammation_nodes = [inflammation]\n",
        "inflammation_neighbors = list(graph.predecessors(inflammation)) + list(graph.successors(inflammation))\n",
        "inflammation_subgraph = graph.subgraph(inflammation_nodes + inflammation_neighbors)\n",
        "\n",
        "print(f\"Inflammation subgraph: {inflammation_subgraph.number_of_nodes()} nodes, {inflammation_subgraph.number_of_edges()} edges\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_8rrbSczn5p",
        "outputId": "8a8f9585-65a3-45e4-98dc-6c439b034840"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "6. Subgraph Analysis\n",
            "----------------------\n",
            "Inflammation subgraph: 3 nodes, 2 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n7. Advanced Graph Querying\")\n",
        "print(\"-\" * 28)\n",
        "\n",
        "try:\n",
        "    paths = list(nx.all_simple_paths(graph, app, apoptosis, cutoff=3))\n",
        "    print(f\"Paths from APP to apoptosis: {len(paths)}\")\n",
        "    if paths:\n",
        "        print(f\"Shortest path length: {len(paths[0])-1}\")\n",
        "except nx.NetworkXNoPath:\n",
        "    print(\"No paths found between APP and apoptosis\")\n",
        "\n",
        "apoptosis_inducers = list(graph.predecessors(apoptosis))\n",
        "print(f\"Factors that increase apoptosis: {len(apoptosis_inducers)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iys6Oscnzs2P",
        "outputId": "0d1b3c3a-62d3-4725-c048-abc57613ab9b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "7. Advanced Graph Querying\n",
            "----------------------------\n",
            "Paths from APP to apoptosis: 1\n",
            "Shortest path length: 3\n",
            "Factors that increase apoptosis: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n8. Data Export and Visualization\")\n",
        "print(\"-\" * 35)\n",
        "\n",
        "adj_matrix = nx.adjacency_matrix(graph)\n",
        "node_labels = [str(node) for node in graph.nodes()]\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "pos = nx.spring_layout(graph, k=2, iterations=50)\n",
        "nx.draw(graph, pos, with_labels=False, node_color='lightblue',\n",
        "        node_size=1000, font_size=8, font_weight='bold')\n",
        "plt.title(\"BEL Network Graph\")\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "centralities = list(degree_centrality.values())\n",
        "plt.hist(centralities, bins=10, alpha=0.7, color='green')\n",
        "plt.title(\"Degree Centrality Distribution\")\n",
        "plt.xlabel(\"Centrality\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "functions = list(node_types.keys())\n",
        "counts = list(node_types.values())\n",
        "plt.pie(counts, labels=functions, autopct='%1.1f%%', startangle=90)\n",
        "plt.title(\"Node Type Distribution\")\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "relations = list(edge_types.keys())\n",
        "rel_counts = list(edge_types.values())\n",
        "plt.bar(relations, rel_counts, color='orange', alpha=0.7)\n",
        "plt.title(\"Relationship Types\")\n",
        "plt.xlabel(\"Relation\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "id": "h0vvZ00bxWOw",
        "outputId": "a907f287-4b33-436e-9bbe-199decf46ee6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "8. Data Export and Visualization\n",
            "-----------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}